Unprofitable Affiliates and Income Shifting Behavior
Bibliographic record
Abstract
ABSTRACT Income shifting from high-tax to low-tax jurisdictions is considered a primary method of reducing worldwide tax burdens of multinational firms. Current losses also affect income shifting incentives. We extend prior approaches by explicitly considering unprofitable affiliates and test whether the association between losses and tax incentives for unprofitable affiliates deviates from the negative association observed in profitable affiliates. Results suggest that multinational firms alter the distribution of reported profits to take advantage of losses. Our point estimate for profitable affiliates implies that an increase of one standard deviation in the tax incentive, C, of an affiliate with an average return on assets of 13.3 is associated with a lower return on assets of 0.5 percentage points. The same change in tax incentive of an unprofitable affiliate is associated with an increase in its return on assets of approximately 0.7 percentage points, holding assets, labor, productivity, and other factors constant. We further document a larger responsiveness to tax incentives between profitable and unprofitable affiliates in high-tax jurisdictions, consistent with predictions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".